Methods of data collection Dr. Brigitte Karigl, Qatar 19 June

Types of methods used to collect data on waste generation and treatment 2 Statistical surveys Administrative or other sources Statistical estimation procedures A combination of the above mentioned methods

Differences between data collection on waste treatment and waste generation 3 Number of operators of treatment facilities is much lower than that of waste generators Treatment facilities are subject to a stricter supervision than generators  more administrative data Treatment facilities are usually unique with regard to waste types treated, technology, capacity… extrapolation from one facility to another is difficult

Statistical surveys 4 Comprehensive surveys (Census) A survey that collects data from the entire population of interest E.g. a census on waste collectors and treaters in order to collect data on waste treatment or on waste generation Sample surveys Probability samples (simple random, systemic random or strata) are used in order to calculate estimates E.g. a sample survey on waste generation covering all industrial and service activities

How to carry out a survey study succesfully? 5 Questionnaire design Contents and layout Introductions and explanatory notes Contact data and futher information Pretesting of the questionnare Accompanying measures Telephone hotline and support Supporting webpage Data base design aiming at better quality of the data Automatic comparisons and plausibility checks Labelling of inconsistent/implausible replies –> to be checked later

Administrative sources 6 Data sets from public institutions (Environment agencies and other supervising authorities) or associations and organisations in the public sector (e.g. producer responsibility organisations) E.g. consignment notes and notifications of imports/exports Annual waste balances of authorized waste collectors and treaters Administrative sources may be used As the core data set For filling in data caps For plausibility checks

Pros and cons of administrative sources 7 Advantages: Burden on respondents is minimized Good coverage of units under administration Data is usually validated for administrative purposes Continuity and high frequency of updating Disadvantages Differences in definitions, classifications and statistical units are possible Restrictions of access to the data Coverage might not be suitable for statistical purposes

Example from Austria: Annual waste balances 8  According to the Waste Balance Sheet Ordinance 2008, Waste collectors and waste processors have to report annual waste balance sheets electronically to the Provincial Governor not later than on 15 March of each year.  Covering pick-ups of waste from other legal entities, deliveries of waste to other legal entities, in-house waste movements and storage level information  Wastes received from initial waste producers shall be reported as total value per type of waste, broken down by the federal province of origin of the waste and by the economic sector of waste producer.

Electronic Data Management in Austria 9 There are several obligations of documentation, record- keeping and reporting imposed on waste holders by the Austrian Waste Management Act of 2002 and its ordinances The Electronic Data Management Environment (EDM) is an integrated e-Government system consisting of Internet applications and databases to support complex processes of documentation, notification, reporting and data analysis related to environmental protection.

EDM Portal 10

11 Emission-Trading Fluorinated Hydrocarbons eWater Emissions Register Radiation Sources European Pollutant & Transfer Register EDM Waste-Management eIncineration eWEEE eShipment eBatteries & Accumulators ePackaging eEoL-Vehicles eWaybill eWaste-Balance eLandfill eLicence eCertificateeCompost Central register of master data eRAS EDM-Environment

Key benefits of the EDM-system 12 Replacement of conventional paper-based records and reports through efficient electronic data management Quick and efficient data transmission Reduction of error sources Avoidance of duplicate information collection Uniform structures of data collection systems All EDM-applications use the same master data (personal and plan data)  compatibility of data Common reference tables and standard classifications for all applications  compatibility of data Comprehensive data analyses based on a data warehouse solution

Statistical estimation methods 13 Estimation of waste generation by waste factors E.g. waste factors, which establish the relation between the production of a certain product and the quantity of waste generated during the prodution can be applied successfully for specific basic products, where stable and strong causal relations exist Indirect determination of waste generation via waste collection and treatment – estimation tools to assign to a certain type of waste the sources For waste treatment, estimation methods should primarily be used to close data gaps (exception: process-specific key factors for ELV and WEEE)

Example from Austria: Estimation of wastes from agriculture 14 Examples for estimation methods: Estimation of wine marc/pomace (t) based on the harvested wine quantities (hl), average quantity of grapes (t) and share of marc in grapes (%) Estimation of plastic waste from fertilizer bags based on the qunatity of fertilizer sold (t), share of fertilizer sold in bags (%), quantity of fertilizerper bag (kg) and net weight of a bag (g) Weinernte in hl kg Trauben/hlAnteil Trester % Trester in t verkaufte Menge [t] Anteil gesackt Füllmenge kg/Sack Säcke Stück Leergewicht je Sack [g] Gesamtanfall Säcke [t] NPK Dünger %

Combination of different sources and methods 15 Data from different sources are often combined to avoid multiple or overlapping data collection Problems related to the combination of different sources/ methods: Risk of double counting or under coverage Incompatibility of the data because of different concepts, definitions and classifications Differences in level of detail and in level of quantity

Further information 16 Manual on waste statistics: t_details/publication?p_product_code=KS-RA t_details/publication?p_product_code=KS-RA

Contact & Information Dr. Brigitte Karigl 17 Umweltbundesamt Waste Statistics Training Workshop Qatar Statistics Authority ■ June 2013